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检索条件"任意字段=Conference on Visual Data Exploration and Analysis"

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exploration of Spatial and Temporal Modeling Alternatives for HOI

Exploration of Spatial and Temporal Modeling Alternatives fo...

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IEEE Winter conference on Applications of Computer Vision (WACV)

作者： Dabral, Rishabh Sarkar, Srijon Reddy, Sai Praneeth Ramakrishnan, Ganesh Indian Inst Technol Dept Comp Sci Mumbai Maharashtra India

ISBN: (纸本)9780738142661

Human-Object Interaction detection from a video clip can be considered as a special case of video-based visual-Relationship Detection wherein the subject must be a human. Specifically, it involves detecting the humans and objects in the clip as well as the interactions between them. Conventionally, the problem has been formulated as a space-time graph inference problem over the video clip features. In this work, we explore alternate spatial approaches for detecting Human-Object Interactions. We consider a hierarchical setup that decouples spatial and temporal aspects of the problem and analyse the impacts of a variety of design choices for the spatial networks. Particularly, to capture spatial relationships in the scene, we analyze the effectiveness of the traditionally used Graph Convolutional Networks against Convolutional Networks and Capsule Networks. Unlike current approaches, we avoid using ground truth data like depth maps or 3D human pose during inference, thus increasing generalization across non-RGBD datasets as well. We demonstrate a comprehensive analysis of the exploration, both quantitatively and qualitatively, while achieving state-of-the-art results in human-object interaction detection (88.9% and 92.6%) and anticipation tasks of CAD-120 and competitive results on image based HOI detection in V-COCO dataset, setting a new benchmark for visual features based approaches.

关键词： visualization Computer vision Three-dimensional displays conferences Pipelines Benchmark testing Feature extraction

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Audio Perception in Robotic Assistance for Human Space exploration: A Feasibility Study

Audio Perception in Robotic Assistance for Human Space Explo...

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IEEE conference on Aerospace

作者： Marco Sewtz Werner Friedl Adrian Bauer Anne Köpken Florian Lay Nicolai Bechtel Peter Schmaus Rudolph Triebel Neal Y. Lii German Aerospace Center (DLR) Institut of Robotics and Mechatronics Weßling Germany

Future crewed missions beyond low earth orbit will greatly rely on the support of robotic assistance platforms to perform inspection and manipulation of critical assets. This includes crew habitats, landing sites or assets for life support and operation. Maintenance and manipulation of a crewed site in extra-terrestrial environments is a complex task and the system will have to face different challenges during operation. While most may be solved autonomously, in certain occasions human intervention will be required. The telerobotic demonstration mission, Surface Avatar, led by the German Aerospace Center (DLR), with partner European Space Agency (ESA), investigates different approaches offering astronauts on board the International Space Station (ISS) control of ground robots in representative scenarios, e.g. a Martian landing and exploration site. In this work we present a feasibility study on how to integrate auditory information into the mentioned application. We will discuss methods for obtaining audio information and localizing audio sources in the environment, as well as fusing auditory and visual information to perform state estimation based on the gathered data. We demonstrate our work in different experiments to show the effectiveness of utilizing audio information, the results of spectral analysis of our mission assets, and how this information could help future astronauts to argue about the current mission situation.

关键词： visualization European Space Agency Low earth orbit satellites Inspection International Space Station Space exploration Task analysis

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Visitors Vis: Interactive Mining of Suspected Medical Insurance Fraud Groups 18th

Visitors Vis: Interactive Mining of Suspected Medical Insura...

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18th CCF conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Dong, Rixin Liu, Hanlin Guo, Xu Zhou, Jiantao College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9789819996360

As medical insurance continues to grow in size, the losses caused by medical insurance fraud cannot be underestimated. Current data mining and predictive techniques have been applied to analyze and explore the health insurance fraud population. However, previous studies only provide summary results and do not show the details of comparative analysis during the visit, resulting in the inability of auditors to quickly identify anomalous behavior. In this paper, we propose a visual analytics system for interactive medical insurance fraud detection to support the exploration and interpretation of different access processes. We propose a weighted MinDL to improve the accuracy of visit pattern classification in the time-series modeling process, and design a data analysis model and visual analysis view based on medical insurance fraud characteristics to reveal and explore the characteristics of medical insurance fraud groups. We collaborate with related organizations to design and implement an interactive visualization for medical insurance fraud group detection using real Medicare data and expert interviews The system is effective and practical in detecting and analyzing medical insurance fraud syndicates. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Health insurance

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16th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 12th International conference on Information visualization Theory and Applications (IVAPP)

作者： Jekic, Nikolina Mutlu, Belgin Schreyer, Manuela Neubert, Steffen Schreck, Tobias Pro2Future GmbH Inffeldgasse 25f A-8010 Graz Austria AMAG Austria Metall AG Lamprechtshausener Str 61 A-5282 Ranshofen Austria Graz Univ Technol Inst Comp Graph & Knowledge Visualisat Inffeldgasse 16c A-8010 Graz Austria Graz Univ Technol Inst Interact Syst & Data Sci Inffeldgasse 16c A-8010 Graz Austria

ISBN: (纸本)9789897584886

Monitoring, analyzing and determining the production quality in a complex and long-running process such as in the aluminum production is a challenging task. The domain experts are often overwhelmed by the flood of data being generated and collected and have difficulties to analyze and interpret the results. Likewise, experts find it difficult to identify patterns in their data that may indicate deviations and anomalies that lead to unstable processes and lower product quality. We aim to support domain experts in the production data exploration and identifying meaningful patterns. The existing research covers a broad spectrum of pattern recognition methodologies that can be potentially applied to elicit patterns in data collected from industrial production. Hence, in this paper, we further analyze the applicability of different similarity measures to effectively recognize specific ultrasonic patterns which may indicate critical process deviations in aluminum production.

关键词： Similarity Measures visual analysis Aluminum Casting

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CNN Based Model for Underwater Image Classification and Enhancement

CNN Based Model for Underwater Image Classification and Enha...

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Advances in Modern Age Technologies for Health and Engineering Science (AMATHE), International conference on

作者： Sahit Jamandlamudi Deva Priya Isravel Julia Punitha Malar Dhas Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The challenge of enhancing underwater images is difficult because of several issues, including color distortion, inadequate illumination, and dispersion from suspended particles in the water. Conventional picture enhancing methods frequently find it difficult to resolve these problems. Thanks to its ability to learn intricate representations and pick up on minute features, deep learning has become a potent tool for picture improvement tasks in recent years. In this work, we suggest a novel deep learning-based method for improving underwater images. To automatically learn and improve the visual quality of underwater scenes, we use a convolutional neural network architecture that is specifically made to handle underwater photos. A sizable dataset of underwater photos is used to train the network, with an emphasis on contrast improvement and color correction. We compare our method with many state-of-the-art techniques and undertake extensive tests on a benchmark dataset to evaluate our approach. The outcomes show that our strategy performs much better than current approaches both in terms of subjective visual quality and objective criteria. Our technique improves visibility, minimizes noise, and successfully restores colors in underwater photos. By presenting a deep learning-based method that enhances the visual quality of underwater photographs, this work advances the field of underwater image processing and aids in marine research, underwater exploration, and surveillance.

关键词： Deep learning visualization Adaptation models Image color analysis Surveillance Lighting Distortion

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Preliminary exploration of Geotechnical data Interpretation and GIS Integration: Toward AI Implementation

Preliminary Exploration of Geotechnical Data Interpretation ...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International conference for

作者： Danah S. Al-Enezi Kuwait Institute for Scientific Research Kuwait

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Kuwait's rapid urban expansion, driven by large-scale projects, such as AI-Mutlaa City and Silk City, highlights the need for comprehensive geotechnical data to support sustainable development. However, detailed studies on the subsurface geotechnical characteristics of the northern regions of Kuwait are lacking. This study focuses on establishing a preliminary geotechnical database, addressing challenges encountered during its development, and conducting a pilot analysis for AI-Mutlaa City. A questionnaire was distributed to geology, environmental, geotechnical, and engineering professionals to identify the challenges associated with creating the database. The survey revealed issues such as inconsistent data formats, limited access to borehole logs, and the hesitancy of private firms to share data due to security concerns. Despite these challenges, borehole data from ministries and private organizations were collected and organized using Microsoft Excel. These data formed the foundation of the geotechnical database, which was analyzed and visualized using Geographic Information System (GIS) tools. The pilot study in AI-Mutlaa City revealed predominantly dense soil layers, with significant variations in soil strength at a depth of 19.5 m, where standard penetration resistance ranged from 10 to 50 blows. These findings emphasize the variability of soil properties in the area and underscore the importance of site-specific investigations. The established geotechnical database provides valuable insights for construction planning, risk assessment, and geohazard management. Moving forward, the integration of artificial intelligence into the database is planned to enhance data processing, automate soil classification, and predict subsurface conditions in an effort to contribute to advanced geotechnical practices in Kuwait.

关键词： Surveys Technological innovation Accuracy databases Urban areas Soil properties visual databases Artificial intelligence Sustainable development Geographic information systems

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Method for identifying geological inhomogeneities during surface seismic exploration based on finite difference numerical modeling 17

Method for identifying geological inhomogeneities during sur...

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17th conference and Exhibition Engineering and Mining Geophysics 2021

作者： Tsarev, R.I. Prigara, A.M. Voroshilov, V.A. JSC VNII Galurgii PSU

ISBN: (纸本)9789462823723

When performing surface seismic surveys in conditions of low contrast of acoustic properties of salt strata, visual interpretation does not work. The aim of the research is to establish relationships between the presence of geological heterogeneities and changes in the attributes of a seismic record. The basis for such studies is the need to create an objective and substantiated approach to the interpretation of seismic data in the study of a halogen formation. The work was carried out on the basis of seismic modeling, the initial data for which were the results of acoustic logging. The interpretation of the data was carried out by means of the attribute analysis of the synthetic sections obtained as a result of modeling. To optimize the process of working with seismic attributes, a special software AtAn has been developed, which allows performing correlation analysis using several calculation schemes. Based on the results of the attribute analysis of theoretical wave fields, obtained by us on the basis of solving the direct problem, a summary table was built, reflecting the effect of geological heterogeneities on the change in seismic attributes. © conference and Exhibition Eng. and Mining Geophysics 2021. All rights reserved.

关键词： Acoustic properties

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Disinformation analysis and Tracking Dashboard

Disinformation Analysis and Tracking Dashboard

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International conference on Military Communications and Information Systems (ICMCIS)

作者： Justina Mandravickaitė Milita Songailaitė Eglė Kankevičiūtė Anton Volčok Tomas Krilavičius Faculty of Informatics Vytautas Magnus University / CARD Lithuania Lingo data Lingo data / CARD Lithuania

The importance of disinformation identification and tracking in the information age is not questionable. War in Ukraine, information attacks in Baltic states, and a number of other disinformation attacks worldwide show that it is becoming a conventional tool in information space. In this paper, we present a disinformation analysis and tracking dashboard. It has 3 main components: Disinformation Analyzer, Match disinformation cases and Model Accuracy. The first component consists of a fined-tuned RoBERTa model for classifying textual input into fake and neutral. The second component contains semantic matching functionality for checking the semantic similarity of input text to the set of known disinformation cases. The third component includes the confusion matrix and model accuracy information. All 3 components have been combined into visual analytics platform for easy inspection and exploration of potential and known disinformation cases. The majority of the work was done during TIDE Hackathon 2023 as a part of the Disinformation Analyzer Challenge.

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visual Analytics for Profiling Land Use Changes

Visual Analytics for Profiling Land Use Changes

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Claudio Santos Maryam Hosseini João Rulff Fabio Miranda Luc Wilson Claudio Silva Nivan Ferreira Marcos Lage Universidade Federal Fluminense Massachusetts Institute of Technology New York University University of Illinois Chicago Universidade Federal de Pernambuco Kohn Pedersen Fox Associates PC

The growth of cities calls for regulations and zoning rules on how each piece of urban space will be used. Tracking land use can reveal a wealth of information about urban development. For that matter, cities have been releasing data sets describing the historical evolution of the shape and the attributes of land units. The complex nature of land-use data, however, makes the analysis of such data challenging and time-consuming. To address these challenges, we propose Urban Chronicles, a visual analytics system that enables interactive exploration of land-use changes. Using New York City's Primary Land Use Tax Lot Output (PLUTO), we show the system's capabilities to explore the data from several years at different scales. UrbanChron-iclessupports on-the-fly aggregation and filtering operations that leverage the hierarchical nature of the data set to index the shape and attributes of geographical regions that change over time. Finally, we demonstrate the system's utility through case studies that analyze the impact of Hurricane Sandy on land use attributes and the effects of rezoning plans in Brooklyn.

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An exploration of Machine Learning Methods for Gait analysis of Potential Guide Dogs 23

An Exploration of Machine Learning Methods for Gait Analysis...

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Proceedings of the Tenth International conference on Animal-Computer Interaction

作者： Yifan Wu Colt Nichols Marc Foster Devon Martin James Dieffenderfer Masataka Enomoto B. Duncan X. Lascelles Jane Russenberger Gerald Brenninkmeyer Alper Bozkurt David L. Roberts Dept of Electrical and Computer Engineering North Carolina State University USA College of Veterinary Medicine North Carolina State University USA International Working Dog Registry USA Guiding Eyes for the Blind USA Dept of Computer Science North Carolina State University USA

ISBN: (纸本)9798400716560

Gait analysis is important for guide dog organizations, as ideal guide dogs have a smooth and efficient gait, where they can also easily shift between and maintain various gaits. Gait quality and natural traveling speed are two of the multiple factors important in matching a guide dog to its visually impaired handler. Gait evaluation typically includes subjective visual observation of the dog or objective assessments obtained from special-designed equipment. Guide dog organizations need a method to easily collect and analyze objective data of gait information. In this work, we explored how various machine learning models could learn and analyze gait patterns from inertial measurements data that were collected during two different data collection experiments using a wearable sensor device. We also evaluated how well each machine learning model could generalize behavior patterns from various dogs under different environments. Additionally, we compared how sensor placement locations could affect gait prediction performance by attaching the sensor device to the dog’s neck and back area respectively. The tested machine learning models were able to classify different gaits in the range of 42% to 91% in terms of accuracy, and predict various gait parameters with an error rate ranging from 14% to 29% depending on the setup. Furthermore, we also observed that using behavior data collected from the neck region contains more movement information than the back area. By performing a cross-dataset generalization test on the machine learning models, we found that even with performance drop, the models were able to learn gait-specific behavior patterns that are generalizable for different dogs. Although the results were preliminary, the proposed gait analysis exploration still showed promising potential for studying behavior patterns of candidate guide dogs.

关键词： Canine Behavior analysis Canine Gait analysis Guide Dog IMU Machine Learning

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